MAREANO - NiN:splitting biogeographic regions_HiDensBelow1500m

Asssumes you have aready run: - config - workflow_deepseaNiN_Start

You now have the objects needed to explore the dataset and find the best way to apply biogeographic splits. This is a pre-step before running the workflow again on the split dataset to explore possible LKMs.

Finding Regional (biogeographic) Splits

This requires exploring the dataset to theorise the best ways to split the data with: - environmental biplots/colourings

then applying the splits.

Libraries

library(plotly)
library(readxl)

Exploring biplots

make full dataset to explore

should include all env Vars and otus to explore, and make species richness variable

#add species data and sp richness variable
env_sub_meta1<-cbind(env,otu_6)
env_sub_meta1$spRich<-rowSums(otu_6[,-c(1:which(colnames(otu_6)=="Zoanthidae"))]!=0)

#rename X
env_sub_meta1$X <- env$X.y
env_sub_meta1 <-env_sub_meta1 %>% select (-c(X.y))

#add samplID
env_sub_meta1$SampID<-envSel$SampID

Add provisional biotope data to env file

Note that biotopes were last assigned in march 2022 and therefore there are some addional samples that have not yet got a biotope assigned. These should just be NAs

biotopeInfo<-read_xlsx(file.path(dataPath, "inputs/MAREANO_provisional_biotope_classification_0322.xlsx"), sheet=1) %>%
  select(-c(x_coordinate_UTM33N, y_coordinate_UTM33N))

env_sub_meta<-left_join(env_sub_meta1,biotopeInfo)

Joining, by = "SampID"

a colour palette for discrete plots (see later in script)

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

Find min max of vars for scales

colnames(env_sub_meta)

  [1] "X"                                     "bathy"                                
  [3] "diffME3"                               "diffME9"                              
  [5] "landscape"                             "msr1_mag"                             
  [7] "msr5_mag"                              "salt_max"                             
  [9] "salt_mean"                             "salt_min"                             
 [11] "salt_std"                              "slope3"                               
 [13] "slope9"                                "spd_max"                              
 [15] "spd_mean"                              "spd_min"                              
 [17] "spd_std"                               "temp_max"                             
 [19] "temp_mean"                             "temp_min"                             
 [21] "temp_std"                              "u_bott_mean"                          
 [23] "v_bott_mean"                           "X.y"                                  
 [25] "Y"                                     "MLDmax_Robinson"                      
 [27] "MLDmean_Robinson"                      "MLDmin_Robinson"                      
 [29] "MLDsd_Robinson"                        "Smax_Robinson"                        
 [31] "Smean_Robinson"                        "Smin_Robinson"                        
 [33] "Ssd_Robinson"                          "Tmax_Robinson"                        
 [35] "Tmean_Robinson"                        "Tmin_Robinson"                        
 [37] "Tsd_Robinson"                          "CDirmax_Robinson"                     
 [39] "CDirmean_Robinson"                     "CDirmin_Robinson"                     
 [41] "CDirsd_Robinson"                       "CSpdmax_Robinson"                     
 [43] "CSpdmean_Robinson"                     "CSpdmin_Robinson"                     
 [45] "CSpdsd_Robinson"                       "Umax_Robinson"                        
 [47] "Umean_Robinson"                        "Umin_Robinson"                        
 [49] "Usd_Robinson"                          "Vmax_Robinson"                        
 [51] "Vmean_Robinson"                        "Vmin_Robinson"                        
 [53] "Vsd_Robinson"                          "BO22_carbonphytoltmax_bdmean"         
 [55] "BO22_carbonphytoltmax_ss"              "BO22_carbonphytoltmin_bdmean"         
 [57] "BO22_carbonphytoltmin_ss"              "BO22_carbonphytomean_bdmean"          
 [59] "BO22_carbonphytomean_ss"               "BO22_carbonphytorange_bdmean"         
 [61] "BO22_carbonphytorange_ss"              "BO22_chloltmax_bdmean"                
 [63] "BO22_chloltmax_ss"                     "BO22_chloltmin_bdmean"                
 [65] "BO22_chloltmin_ss"                     "BO22_chlomean_bdmean"                 
 [67] "BO22_chlomean_ss"                      "BO22_chlorange_bdmean"                
 [69] "BO22_chlorange_ss"                     "BO22_dissoxltmax_bdmean"              
 [71] "BO22_dissoxltmin_bdmean"               "BO22_dissoxmean_bdmean"               
 [73] "BO22_dissoxrange_bdmean"               "BO22_icecoverltmax_ss"                
 [75] "BO22_icecoverltmin_ss"                 "BO22_icecovermean_ss"                 
 [77] "BO22_icecoverrange_ss"                 "BO22_icethickltmax_ss"                
 [79] "BO22_icethickltmin_ss"                 "BO22_icethickmean_ss"                 
 [81] "BO22_icethickrange_ss"                 "BO22_ironltmax_bdmean"                
 [83] "BO22_ironltmin_bdmean"                 "BO22_ironmean_bdmean"                 
 [85] "BO22_ironrange_bdmean"                 "BO22_nitrateltmax_bdmean"             
 [87] "BO22_nitrateltmin_bdmean"              "BO22_nitratemean_bdmean"              
 [89] "BO22_nitraterange_bdmean"              "BO22_phosphateltmax_bdmean"           
 [91] "BO22_phosphateltmin_bdmean"            "BO22_phosphatemean_bdmean"            
 [93] "BO22_phosphaterange_bdmean"            "BO22_ppltmax_bdmean"                  
 [95] "BO22_ppltmax_ss"                       "BO22_ppltmin_bdmean"                  
 [97] "BO22_ppltmin_ss"                       "BO22_ppmean_bdmean"                   
 [99] "BO22_ppmean_ss"                        "BO22_pprange_bdmean"                  
[101] "BO22_pprange_ss"                       "BO22_silicateltmax_bdmean"            
[103] "BO22_silicateltmin_bdmean"             "BO22_silicatemean_bdmean"             
[105] "BO22_silicaterange_bdmean"             "MS_biogeo05_dist_shore_5m"            
[107] "gmorph"                                "sedclass"                             
[109] "cobB"                                  "gravel"                               
[111] "mud"                                   "rock"                                 
[113] "sand"                                  "coords.x1"                            
[115] "coords.x2"                             "optional"                             
[117] "swDensRob_avs"                         "MLDmean_bathy"                        
[119] "MLDmin_bathy"                          "MLDmax_bathy"                         
[121] "Actiniaria.red"                        "Actiniaria.violet"                    
[123] "Actiniaria.white"                      "Actiniaria.yellow"                    
[125] "Actiniaria_buried.redish"              "Actiniaria_buried.yellow"             
[127] "Actiniaria_buried_dark"                "Actiniaria_epizoic"                   
[129] "Actiniaria_yellow_stolon"              "Actinostola_callosa"                  
[131] "Alcyonidium_sp."                       "Alcyonium_digitatum"                  
[133] "Amphicteis_ninonae"                    "Antedonoidea"                         
[135] "Antho_dichotoma"                       "Anthomastus_sp."                      
[137] "Anthothela_grandiflora"                "Aphroditidae"                         
[139] "Aporrhais_sp."                         "Arctica_islandica"                    
[141] "Asbestopluma_furcata"                  "Asbestopluma_pennatula"               
[143] "Ascidia_sp."                           "Ascidia_sp..transparent"              
[145] "Ascidia_sp._veined"                    "Ascidia_sp._violet"                   
[147] "Ascidia_virginea"                      "Ascidiacea_colonial_encrusting"       
[149] "Ascidiacea_colonial_encrusting.orange" "Ascidiacea_colonial_encrusting.white" 
[151] "Ascidiacea_colonial_erect"             "Ascidiacea_solitary"                  
[153] "Ascidiacea_solitary_big"               "Asconema_setubalense"                 
[155] "Astacidea"                             "Asterias_rubens"                      
[157] "Asteronyx_loveni"                      "Asterozoa"                            
[159] "Astropecten_irregularis"               "Astropectinidae"                      
[161] "Axinella_infundibuliformis"            "Axinellidae"                          
[163] "Bacterial_mat"                         "Balanus_balanus"                      
[165] "Balticina_sp."                         "Bathybiaster_vexillifer"              
[167] "Bathycrinus_carpenterii"               "Bathyplotes_natans"                   
[169] "Beringius_sp."                         "Bolocera_tuediae"                     
[171] "Bonelliidae"                           "Botryllus_sp."                        
[173] "Bourgueticrinina"                      "Brada_sp."                            
[175] "Bryozoa_calcareous_branched"           "Bryozoa_coral"                        
[177] "Bryozoa_encrusting"                    "Bryozoa_soft_branched"                
[179] "Bryozoa_soft_bush"                     "Buccinidae"                           
[181] "Buccinum_hydrophanum"                  "Buccinum_sp."                         
[183] "Buccinum_undatum"                      "Bugula_sp."                           
[185] "Cancer_pagurus"                        "Candelabrum_sp."                      
[187] "Caulophacus_arcticus"                  "Celleporidae"                         
[189] "Ceramaster.Hippasterias"               "Ceramaster_granularis"                
[191] "Cerianthidae"                          "Cerianthidae.cup_coral"               
[193] "Cerianthidae.dark"                     "Cerianthidae.violet"                  
[195] "Cerianthus_lloydii"                    "Cerianthus_vogti"                     
[197] "Chaetopterus_sp."                      "Chelonaplysilla_sp."                  
[199] "Chionoecetes_opilio"                   "Chlamys_sp."                          
[201] "Chondrocladia_gigantea"                "Cidaris_cidaris"                      
[203] "Ciona_intestinalis"                    "Cirripedia"                           
[205] "Cladorhiza_gelida"                     "Cladorhiza_sp."                       
[207] "Cladorhizidae"                         "Cladorhizidae_bottlebrush"            
[209] "Cladorhizidae_branched"                "Cladorhizidae_bush"                   
[211] "Cladorhizidae_stalked"                 "Clavularia_borealis"                  
[213] "Clavulariidae"                         "Colossendeis_angusta"                 
[215] "Colossendeis_proboscidea"              "Colossendeis_sp."                     
[217] "Colus_sp."                             "Conocrinus_lofotensis"                
[219] "Corella_parallelogramma"               "Corymorpha_glacialis"                 
[221] "Corymorpha_nutans"                     "Corymorpha_sand_stolon"               
[223] "Corymorpha_sp."                        "Crangonidae"                          
[225] "Craniella_cranium"                     "Craniella_sp."                        
[227] "Craniella_zetlandica"                  "Crisia_sp."                           
[229] "Crossaster_papposus"                   "Crossaster_sp."                       
[231] "Crossaster_squamatus"                  "Ctenodiscus_crispatus"                
[233] "Ctenophora_benthic"                    "Cucumaria_frondosa"                   
[235] "Cup_coral"                             "Dallina_septigera"                    
[237] "Dendrobeania_sp."                      "Dendrodoa_aggregata"                  
[239] "Dendronotus_sp."                       "Didemnidae"                           
[241] "Diplopteraster_multipes"               "Ditrupa_arietina"                     
[243] "Drifa_glomerata"                       "Duva_florida"                         
[245] "Dysidea_fragilis"                      "Echinoidea_irregular"                 
[247] "Echinoidea_regular"                    "Echinus_esculentus"                   
[249] "Echinus_sp."                           "Ectopleura_larynx"                    
[251] "Edwardsiidae"                          "Elpidia_glacialis"                    
[253] "Enteropneusta"                         "Eucratea_loricata"                    
[255] "Filograna_implexa"                     "Flustridae"                           
[257] "Flustrina"                             "Foraminifera_calcareous"              
[259] "Funiculina_quadrangularis"             "Geodia.Stelleta"                      
[261] "Geodia.Stryphnus"                      "Geodia_atlantica"                     
[263] "Geodia_barretti"                       "Geodia_macandrewii"                   
[265] "Geodia_phlegraei"                      "Geodia_sp."                           
[267] "Gersemia_rubiformis"                   "Geryon_trispinosus"                   
[269] "Gorgonacea"                            "Gorgonocephalus_sp."                  
[271] "Gracilechinus_acutus"                  "Gracilechinus_sp."                    
[273] "Grantia_compressa"                     "Halcampa_arctica"                     
[275] "Halcampa_sp."                          "Halcampoides_sp."                     
[277] "Halecium_sp."                          "Halichondria_sp."                     
[279] "Haliclona_sp."                         "Hamacantha_bowerbanki"                
[281] "Heliometra_glacialis"                  "Henricia_sp."                         
[283] "Henricia_sp..blue"                     "Henricia_sp..orange"                  
[285] "Henricia_sp..red"                      "Henricia_sp..violet"                  
[287] "Henricia_sp..white"                    "Henricia_sp..yellow"                  
[289] "Hexactinellida"                        "Hexactinellida_fan_shaped"            
[291] "Hexactinellida_parabol"                "Hexactinellida_urn.shaped"            
[293] "Hexadella_dedritifera"                 "Hippasteria_phrygiana"                
[295] "Hirudinea"                             "Hormathia_digitata"                   
[297] "Hormathia_nodosa"                      "Hormathia_sp."                        
[299] "Hormathiidae"                          "Horneridae"                           
[301] "Hyalonema_sp."                         "Hyas_coarctatus"                      
[303] "Hyas_sp."                              "Hydroides_norvegica"                  
[305] "Hydrozoa_feather"                      "Hydrozoa_solitary"                    
[307] "Hydrozoa_tree"                         "Hymedesmia_paupertas"                 
[309] "Hymenaster_pellucidus"                 "Hymenodiscus_coronata"                
[311] "Icasterias_panopla"                    "Isidella_lofotensis"                  
[313] "Isodictya_palmata"                     "Jasmineira_sp."                       
[315] "Kinetoskias_smitti"                    "Kolga_hyalina"                        
[317] "Kophobelemnon_stelliferum"             "Kukenthalia_borealis"                 
[319] "Lafoea_sp."                            "Laminaria_sp."                        
[321] "Laminariales"                          "Lanice_conchilega"                    
[323] "Latrunculia_sp."                       "Leieschara_sp."                       
[325] "Leptasterias_muelleri"                 "Leptasterias_sp."                     
[327] "Leptychaster_arcticus"                 "Lichenoporidae"                       
[329] "Liponema_multicorne"                   "Lithodes_maja"                        
[331] "Lithodidae"                            "Lophaster_furcifer"                   
[333] "Lophelia_pertusa"                      "Lucernaria_bathyphila"                
[335] "Luidia_ciliaris"                       "Luidia_sp."                           
[337] "Madrepora_oculata"                     "Mellonympha_mortenseni"               
[339] "Mesothuria_intestinalis"               "Molpadia_sp."                         
[341] "Molva_sp."                             "Munida_sarsi"                         
[343] "Munida_sp."                            "Munidopsis_serricornis"               
[345] "Muriceides_kuekenthali"                "Mycale_lingua"                        
[347] "Myxicola_sp."                          "Myxilla_incrustans"                   
[349] "Nemertea"                              "Nemertesia_antennina"                 
[351] "Neohela_sp."                           "Nephrops_norvegicus"                  
[353] "Nephtheidae"                           "Neptunea_despecta"                    
[355] "Neptunea_sp."                          "Nereididae"                           
[357] "Nothria_sp."                           "Oceanapia_robusta"                    
[359] "Ophiacanthidae"                        "Ophiocten_gracilis"                   
[361] "Ophiocten_sericeum"                    "Ophiocten_sp."                        
[363] "Ophiopholis_aculeata"                  "Ophiopleura_borealis"                 
[365] "Ophioscolex_glacialis"                 "Opisthobranchia"                      
[367] "Pachycerianthus_multiplicatus"         "Paguridae"                            
[369] "Paragorgia_arborea"                    "Paralithodes_camtschaticus"           
[371] "Paramuricea_placomus"                  "Parasmittina_jeffreysi"               
[373] "Parastichopus_tremulus"                "Patellogastropoda"                    
[375] "Pectinariidae"                         "Pectinidae"                           
[377] "Peltaster_placenta"                    "Pennatula_phosphorea"                 
[379] "Pennatulacea"                          "Phakellia.Axinella"                   
[381] "Phakellia_sp."                         "Phyllodoce_rosea"                     
[383] "Phyllodocidae"                         "Plicatellopsis_bowerbanki"            
[385] "Polycarpa_sp."                         "Polychaeta_fishingnet"                
[387] "Polychaeta_question_mark"              "Polychaeta_sediment_tube"             
[389] "Polychaeta_soft_thin_tube"             "Polychaeta_tube"                      
[391] "Polymastia_grimaldii"                  "Polymastia_sp."                       
[393] "Polymastiidae"                         "Polynoidae"                           
[395] "Pontaster_tenuispinus"                 "Porania.Poraniomorpha"                
[397] "Porania_sp."                           "Poraniidae"                           
[399] "Poraniomorpha_sp."                     "Poraniomorpha_tumida"                 
[401] "Porella_compressa"                     "Porella_sp."                          
[403] "Porifera_bat"                          "Porifera_big"                         
[405] "Porifera_branched"                     "Porifera_brown_papillae"              
[407] "Porifera_cupcake"                      "Porifera_dirty_yellow"                
[409] "Porifera_egg"                          "Porifera_encrusting"                  
[411] "Porifera_encrusting.bluegrey"          "Porifera_encrusting.brown"            
[413] "Porifera_encrusting.green"             "Porifera_encrusting.grey"             
[415] "Porifera_encrusting.orange"            "Porifera_encrusting.purple"           
[417] "Porifera_encrusting.red"               "Porifera_encrusting.white"            
[419] "Porifera_encrusting.yellow"            "Porifera_erect"                       
[421] "Porifera_fan"                          "Porifera_fan.big"                     
[423] "Porifera_fan.small"                    "Porifera_fan.white"                   
[425] "Porifera_lily"                         "Porifera_lollipop"                    
[427] "Porifera_medium.white"                 "Porifera_medium.yellow"               
[429] "Porifera_medium_round"                 "Porifera_parabol"                     
[431] "Porifera_small.green"                  "Porifera_small.irregular"             
[433] "Porifera_small.orange"                 "Porifera_small.round_yellow"          
[435] "Porifera_small.spikey"                 "Porifera_small.stalked"               
[437] "Porifera_small.yellow"                 "Porifera_string"                      
[439] "Porifera_urn"                          "Porifera_white_bush"                  
[441] "Porifera_window"                       "Pourtalesia_jeffreysi"                
[443] "Primnoa_resedaeformis"                 "Protanthea_simplex"                   
[445] "Pseudamussium_peslutrae"               "Pseudarchaster_parelii"               
[447] "Psolus_phantapus"                      "Psolus_sp."                           
[449] "Psolus_squamatus"                      "Pteraster_militaris"                  
[451] "Pteraster_obscurus"                    "Pteraster_pulvillus"                  
[453] "Pteraster_sp."                         "Ptychogastria_polaris"                
[455] "Quasillina_brevis"                     "Quasillina_sp."                       
[457] "Radicipes_sp."                         "Reteporella_beaniana"                 
[459] "Reteporella_sp."                       "Sabellidae"                           
[461] "Saduria_sp."                           "Scaphopoda"                           
[463] "Sclerocrangon_ferox"                   "Serpulidae"                           
[465] "Sertulariidae"                         "Siboglinidae"                         
[467] "Smittinidae"                           "Solaster_endeca"                      
[469] "Solaster_sp."                          "Solasteridae"                         
[471] "Spatangoida"                           "Spatangus_purpureus"                  
[473] "Spiochaetopterus_tubes"                "Spionidae"                            
[475] "Spirobranchus_triqueter"               "Spirontocaris_sp."                    
[477] "Stauromedusae"                         "Steletta_grubei"                      
[479] "Stelletta_sp."                         "Stichastrella_rosea"                  
[481] "Strongylocentrotus_sp."                "Stryphnus_ponderosus"                 
[483] "Styela_sp."                            "Stylasteridae"                        
[485] "Stylocordyla_borealis"                 "Swiftia_sp."                          
[487] "Sycon_sp."                             "Sycon_stalked"                        
[489] "Tentorium_semisuberites"               "Terebellida"                          
[491] "Tetilla_sp."                           "Thenea_abyssorum"                     
[493] "Thenea_levis"                          "Thenea_sp."                           
[495] "Thuiaria_obsoleta"                     "Thuiaria_thuja"                       
[497] "Tremaster_mirabilis"                   "Tubularia_indivisa"                   
[499] "Tubularia_sp."                         "Tubulariidae"                         
[501] "Tunicata_trunk"                        "Umbellula_encrinus"                   
[503] "Urasterias_lincki"                     "Urticina_sp."                         
[505] "Virgularia_mirabilis"                  "Weberella_bursa"                      
[507] "Zoanthidae"                            "gnmds1"                               
[509] "gnmds2"                                "dca1"                                 
[511] "dca2"                                 

Plots with CONTINUOUS colour scales (variables)

Bathy v Temp w gnmds ax 1

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by gnmds r6 ax1")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_nmds1.png"),
       device = "png",
       dpi=300 )

Saving 7 x 7 in image

Bathy v Density w gnmds ax 1

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = swDensRob_avs,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Density vs Bathy - coloured by gnmds1")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_bathy_v_dens_gnmds1.png"),
       device = "png",
       dpi=300 )

Saving 7 x 7 in image

Bathy v Temp w longitude

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = X.y),
             size = 1) +
  scale_colour_gradientn(limits = c(-107939, 1162261),
                        colors=c('red','yellow','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by longitude")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_longitude.png"),
       device = "png",
       dpi=300 )

Bathy v Temp w latitude

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = Y),
             size = 1) +
  scale_colour_gradientn(limits = c(6944134, 8949734),
                        colors=c('red','yellow','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by latitude")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_latitude.png"),
       device = "png",
       dpi=300 )

Bathy v Temp w iceCovLTMax

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0,0.93),
                        colors=c('grey','turquoise','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by ice cover LT max")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_iceCovLTmax.png"),
       device = "png",
       dpi=300 )

Bathy v Salinity coloured by temp

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = Tmean_Robinson),
             size = 1) +
  scale_colour_gradientn(limits = c(-1.1, 8.5),
                        colors=c('red','yellow','green'))+
  ggtitle("Salinity (max Robinson) vs Bathy - coloured by av Temp (R)")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_salMax_temp.png"),
       device = "png",
       dpi=300 )

Salinity v Temp gnmds1

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Salinity (max R) - coloured by gnmds r6 ax 1 - grey 2.5-5")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_nmds1.png"),
       device = "png",
       dpi=300 )

Salinity v Temp dissoxmean

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = BO22_dissoxmean_bdmean),
             size = 1) +
scale_colour_gradientn(limits = c(282.5, 372.2),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Salinity (max R) - coloured by dissolved oxygen")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_disooxmean.png"),
       device = "png",
       dpi=300 )

TS - icecoverLTMax

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0,0.93),
                        colors=c('blue','green','red'))+
  ggtitle("Temp (AvR) v Salinity (maxR) - coloured by ice cover LT max")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_iceCoveLTmax.png"),
       device = "png",
       dpi=800 )

X v Y - gnmds1

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Geography (X v Y) - coloured by gnmds1")

ggplotly(tb_ax1)

NA

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_gnmds1.png"),
       device = "png",
       dpi=300 )

Saving 7 x 7 in image

X v Y - density

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = swDensRob_avs),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$swDensRob_avs), 
                                    max(env_sub_meta$swDensRob_avs)),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by water density")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_swDenRobavs.png"),
       device = "png",
       dpi=800 )

Saving 7 x 7 in image

X v Y - dissox

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = BO22_dissoxmean_bdmean),
             size = 1) +
  scale_colour_gradientn(limits = c(282,373),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by dissovled oxygen")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_dissox.png"),
       device = "png",
       dpi=800 )

X v Y - icecoverLTMax

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0.00001,0.93),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by ice cover LT max - grey <0.00001")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_iceCoveLTmax.png"),
       device = "png",
       dpi=800 )

Temp v disoxltmin gnmds

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = BO22_dissoxmean_bdmean)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(-2.3, 2),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) v disox mean - coloured by gnmds ax 1")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_TmeanR_v_dissoxltmin_gnmds1.png"),
       device = "png",
       dpi=300 )

Temp v disoxltmin bathy

tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = BO22_dissoxmean_bdmean)) +
  theme_classic() +
  geom_point(aes(colour = bathy),
             size = 1) +
  scale_colour_gradientn(limits = c(-702, -38),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) v disox mean - coloured by bathymetry")

tb_ax1

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mTmeanR_v_dissoxltmin_bathy.png"),
       device = "png",
       dpi=300 )

Plots with DISCRETE colour scales (variables)

X v Y with mld-bathy categories

dis_split <- ggplot(data = env,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = MLDmean_bathy),
             size = 1) +
  scale_colour_manual(values=cbPalette)+
 # scale_colour_brewer(palette = "Set3") +
  ggtitle("Easting vs Northing - coloured by Mixed layer depth proximity")

dis_split

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_MLDmeanBathy.png"),
       device = "png",
       dpi=300 )

categorise gnmds

env_sub_meta$ax1cat<-cut(env_sub_meta$gnmds1, 
      breaks=c(-3.2,-3,-2,-1,0,1,2,3,3.46))

env_sub_meta$ax2cat<-cut(env_sub_meta$gnmds2, 
      breaks=c(-1.9,-1,0,1,2,3,4,4.9))

X v Y with gnmds ax 1 as HC categories

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = ax1cat),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Spectral") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by gnmds axis 1 HC units")

dis_split

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_ax1cat.png"),
       device = "png",
       dpi=300 )

categorise temp 5.1

env_sub_meta$temp5_1<-cut(env_sub_meta$Tmean_Robinson, 
      breaks=c(-1.1, 5.1, 8.5))
#

X v Y with temp 5.1

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = temp5_1),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by temp thresholded at 5.1*C")

dis_split

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_temp5_1.png"),
       device = "png",
       dpi=300 )

categorise dissox

env_sub_meta$dissoxav305<-cut(env_sub_meta$BO22_dissoxmean_bdmean, 
     # breaks=c(256, 282, 360)) #ltmin
     breaks=c(282.5,305,372.2),
     labels=c("lowO2","hiO2")) #mean
#

X v Y with dissox

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = dissoxav305),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by dissox av thresholded at 305")

dis_split

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_dissoxav305.png"),
       device = "png",
       dpi=300 )

categorise iceCovLTMax

env_sub_meta$iceMx_gt0<-cut(env_sub_meta$BO22_icecoverltmax_ss, 
      breaks=c(0, 0.00001, 0.92838))
#

X v Y with temp 5.1

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = iceMx_gt0),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by ice LT Max thresholded at >0")

dis_split

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_iceLTmx_gt0.png"),
       device = "png",
       dpi=300 )

X v Y with gnmds ax 2 as HC categories

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = ax2cat),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Spectral") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by gnmds axis 2 HC units")

dis_split

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_ax2cat.png"),
       device = "png",
       dpi=300 )

Gnmds w temp 5.1

t_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by temp 5.1 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(temp5_5))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

t_gmo

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_temp5_1.png"),
       device = "png",
       dpi=300 )

Gnmds w dissox 305

o_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by dissox 305 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(dissox305))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

o_gmo

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_dissox305.png"),
       device = "png",
       dpi=300 )

Gnmds w iceLT max >0

i_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by ice max >0 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(iceMx_gt0))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

i_gmo

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_iceMxgt0.png"),
       device = "png",
       dpi=300 )

comp<-i_gmo+o_gmo+t_gmo

Save the plot

##### Save some outputs

ggexport(comp,
          filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_compare.png"),
          width = 1500,
          height = 500)

X v Y with landscape

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = as.factor(landscape)),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by landscape")

dis_split

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_landscape.png"),
       device = "png",
       dpi=300 )

Saving 7 x 7 in image

GNMDS PLOTS

colour palette to cope with up to 25 categorical colours

c25 <- c(
  "dodgerblue2", "#E31A1C", # red
  "green4",
  "#6A3D9A", # purple
  "#FF7F00", # orange
  "black", "gold1",
  "skyblue2", "#FB9A99", # lt pink
  "palegreen2",
  "#CAB2D6", # lt purple
  "#FDBF6F", # lt orange
  "gray70", "khaki2",
  "maroon", "orchid1", "hiDens_b1500pink1", "blue1", "steelblue4",
  "darkturquoise", "green1", "yellow4", "yellow3",
  "darkorange4", "brown"
)

gnmds with density categories

summary(env_sub_meta$swDensRob_avs)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1035    1036    1037    1037    1038    1041 

env_sub_meta$densCat<-cut(env_sub_meta$swDensRob_avs, 
      breaks=c(1035,1036,1037,1038,1039,1040,1041))

p_dens <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by density bins") +
  geom_point(aes(colour = factor(densCat))) +
   scale_colour_manual(values=c25)+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_dens)

NA

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_densityCat.png"),
       device = "png",
       dpi=300 )

Saving 7 x 7 in image

gnmds with density 1036 threshold

summary(env_sub_meta$swDensRob_avs)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1035    1036    1037    1037    1038    1041 

env_sub_meta$densCat1036<-cut(env_sub_meta$swDensRob_avs, 
      breaks=c(1035,1036,1041))

p_dens <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by density threshold 1036") +
  geom_point(aes(colour = factor(densCat1036))) +
   scale_colour_manual(values=c25)+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_dens)

NA

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_densityCat1036.png"),
       device = "png",
       dpi=300 )

Saving 7 x 7 in image

gnmds with bathy categories

summary(env_sub_meta$bathy)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  -2717   -2145   -1954   -1955   -1652   -1504 

env_sub_meta$bathyCat<-cut(env_sub_meta$bathy, 
      breaks=c(-2717,-2600,-2500,-2400,-2300,-2200,-2100,-2000,-1900,-1800,-1700,-1600,-1504),
      labels=c("> 2600m","2500-2600m","2400-2500m","2300-2400m","2200-2300m","2100-2200m",
               "2000-2100m","1900-2000m","1800-1900m","1700-1800m",
               "1600-1700m","< 1600m"))

p_bath <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sediment class") +
  geom_point(aes(colour = bathyCat)) +
  scale_fill_binned()+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_bath)

NA
NA

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_bahtyCat.png"),
       device = "png",
       dpi=300 )

Saving 7 x 7 in image

gnmds with taxa

p_bath <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sediment class") +
  geom_point(aes(colour = as.factor(Umbellula_encrinus))) +
  scale_fill_binned(type = "viridis")+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_bath)

NA
NA

Save the plot

ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_bahtyCat.png"),
       device = "png",
       dpi=300 )

Save environment

EDIT AREA FIRST!

save.image("I:/Scripts/deepseaNiN/Renv_deepseaNiN_HiDensBelow1500m.RData") # edit area first

NOW CLEAR ENVIRONMENT BEFORE RUNNING FOR NEW AREA